R Notebook

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.6     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1
Warning: package ‘ggplot2’ was built under R version 4.2.1Warning: package ‘dplyr’ was built under R version 4.2.1── Conflicts ───────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(lubridate)

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union

unclass(date_time1)

[1] 1577836800
attr(,"tzone")
[1] ""

date_time2 <- as.POSIXlt("2020-01-01")
date_time2

[1] "2020-01-01 GMT"

unclass(date_time2)

$sec
[1] 0

$min
[1] 0

$hour
[1] 0

$mday
[1] 1

$mon
[1] 0

$year
[1] 120

$wday
[1] 3

$yday
[1] 0

$isdst
[1] 0

$zone
[1] "GMT"

$gmtoff
[1] NA

Column data types: Date: “” Time: “” Date-time: “”

ymd_h("2019-01-19 10")

[1] "2019-01-19 10:00:00 UTC"

netflix %>% 
  mutate(date_added = mdy(date_added))

NA

Extracting individual components

netflix <- netflix %>% 
  mutate(day = day(date_added),
         month = month(date_added, label = TRUE, abbr = FALSE),
         year = year(date_added),
         weekday = wday(date_added, label = TRUE))

netflix

NA

Calculating time spans

Three types of time span

‘Difftime’: a datetime object that represents days ‘Period’: represents a timespan like week, month, day ‘Duration’: represents an exact number of seconds

netflix <- netflix %>% 
  mutate(time_period = as.period(today() - date_added),
         time_years = as.numeric(time_period, "years"),
         time_hours = as.numeric(time_period, "hours"),
         time_minutes = as.numeric(time_period, "minutes"),
         time_seconds = as.numeric(time_period, "seconds"),
         time_weeks = as.numeric(time_period, "weeks"),
         time_months = as.numeric(time_period, "months"))

netflix

NA

Subtracting periods from dates

netflix <- netflix %>% 
  mutate(promo_date = date_added - weeks(2),
         removal_date = date_added + month(4))

netflix

Durations

can also play around with dseconds(), dminutes(), dhours(), ddays(), dmonths(), and dyears()

What date is a year after the 10th January 2020

ymd(20200110) + dyears(1)

[1] "2021-01-09 06:00:00 UTC"

Understanding timezones

Lab

Question 1

Question 2

Question 3

July is the most popular month to start the TdF.

Question 4

Saturday is the most common week day to start the TdF.

Question 5

Question 6

Question 7

arrive_time

[1] "2018-08-08 05:40:00 BST"

Question 8

diff_time

[1] 24.41667

Question 9

departure_time <- ymd_hm("2018-08-10 06:20", tz = "Europe/London")
flight_time <- hm("28:46")

departure_time

[1] "2018-08-10 06:20:00 BST"

flight_time

[1] "28H 46M 0S"

arrival_time <- with_tz(departure_time + flight_time, tzone = "Australia/Sydney")

arrival_time

[1] "2018-08-11 20:06:00 AEST"